The drug development timeline currently takes approximately 15 years and over $1.2 billion dollars for a successful nervous system drug. At every stage of this lengthy and expensive process, from laboratory research to clinical trials, there is a high failure rate due to unforeseen biological interactions. In the case of degenerative diseases such as Alzheimer's or Parkinson's disease, no drug has yet been able to stop the progressive degeneration of neurons, and most drugs only ameliorate the symptoms rather than impact the disease process. While the rate of biological research, particularly in the field of neuroscience, has grown tremendously, neuroscientists have been limited by the availability of computational tools that allow them to put together the data in a cohesive manner. The fact that the bottleneck of research has become understanding and predicting interactions is a major problem for the field.
Computational tools that simulate molecular interactions within neurons are very limited. In fact, most neuroscientists today use static pathway maps such as KEGG (Kyoto Encyclopedia of Genes and Genomes) Pathways to visualize the molecular interactions within the neuron, which is a major limitation since molecular interactions within neurons are extremely dynamic, both spatially and temporally. Furthermore, once hypotheses are formed, they are tested in the laboratory, which allows measurement or visualization only of small area or time point. Computer modeling and simulation of the interactions can go well beyond static pathway maps and will allow scientists not only to visualize molecular interactions, but to test hypotheses in the computer before going into the laboratory. Effective computer modeling would accelerate research and reduce the occurrence of “negative data.” The few molecular dynamics simulation platforms to date have required significant software development capability and provided very limited representation of interactions. Other simulation platforms have made headway in predicting interactions between neurons but have yet to tackle molecular pathways within a neuron.